Publications

Clinical tissue specimens are often unscreened, and preparation of tissue sections for analysis by mass spectrometry imaging (MSI) can cause aerosolization of particles potentially carrying an infectious load. We here present a decontamination approach based on ultraviolet-C (UV-C) light to inactivate clinically relevant pathogens such as herpesviridae, papovaviridae human immunodeficiency virus, or SARS-CoV-2, which may be present in human tissue samples while preserving the biodistributions of analytes within the tissue. High doses of UV-C required for high-level disinfection were found to cause oxidation and photodegradation of endogenous species. Lower UV-C doses maintaining inactivation of clinically relevant pathogens to a level of increased operator safety were found to be less destructive to the tissue metabolome and xenobiotics. These doses caused less alterations of the tissue metabolome and allowed elucidation of the biodistribution of the endogenous metabolites. Additionally, we were able to determine the spatially integrated abundances of the ATR inhibitor ceralasertib from decontaminated human biopsies using desorption electrospray ionization-MSI (DESI-MSI).

Purpose: To compare carbon-13 (¹³C) MRSI of hyperpolarized [1-¹³C]pyruvate metabolism in a murine tumor model with mass spectrometric (MS) imaging of the corresponding tumor sections in order to cross validate these metabolic imaging techniques and to investigate the effects of pyruvate delivery and tumor lactate concentration on lactate labeling.

Methods: [1-¹³C]lactate images were obtained from tumor-bearing mice, following injection of hyperpolarized [1-¹³C]pyruvate, using a single-shot 3D ¹³C spectroscopic imaging sequence in vivo and using desorption electrospray ionization MS imaging of the corresponding rapidly frozen tumor sections ex vivo. The images were coregistered, and levels of association were determined by means of Spearman rank correlation and Cohen kappa coefficients as well as linear mixed models. The correlation between [1-¹³C]pyruvate and [1-¹³C]lactate in the MRS images and between [¹²C] and [1-¹³C]lactate in the MS images were determined by means of Pearson correlation coefficients.

Results: [1-¹³C]lactate images generated by MS imaging were significantly correlated with the corresponding MRS images. The correlation coefficient between [1-¹³C]lactate and [1-¹³C]pyruvate in the MRS images was higher than between [1-¹³C]lactate and [¹²C]lactate in the MS images.

Conclusion: The inhomogeneous distribution of labeled lactate observed in the MRS images was confirmed by MS imaging of the corresponding tumor sections. The images acquired using both techniques show that the rate of ¹³C label exchange between the injected pyruvate and endogenous tumor lactate pool is more correlated with the rate of pyruvate delivery to the tumor cells and is less affected by the endogenous lactate concentration.

Discovery: The Rosetta team in Nature Genetics have demonstrated that the amino acid transporter SLC7A5 is required for efficient growth of KRAS-mutant colorectal cancer (CRC). SLC7A5 mediates the transmembrane trafficking of glutamine in exchange for essential amino acids that sustain cell growth, also referred to as an antiporter* in the paper, and has been shown to be critical for tumour development in early and late-stage mouse models of CRC.

Methods: This was achieved using a combination of approaches, including 3D cell culture (organoids), the generation of genetically engineered mouse models (GEMMs) [early and late stage carcinoma] and the analysis of human CRC samples. 

*An antiporter (also called exchanger or counter-transporter) is a cotransporter and integral membrane protein involved in secondary active transport of two or more different molecules or ions across a phospholipid membrane such as the plasma membrane in opposite directions, one into the cell and one out of the cell.

Mass spectrometry imaging can produce large amounts of complex spectral and spatial data. Such data sets are often analyzed with unsupervised machine learning approaches, which aim at reducing their complexity and facilitating their interpretation. However, choices made during data processing can impact the overall interpretation of these analyses. This work investigates the impact of the choices made at the peak selection step, which often occurs early in the data processing pipeline. The discussion is done in terms of visualization and interpretation of the results of two commonly used unsupervised approaches: t-distributed stochastic neighbor embedding and k-means clustering, which differ in nature and complexity. Criteria considered for peak selection include those based on hypotheses (exemplified herein in the analysis of metabolic alterations in genetically engineered mouse models of human colorectal cancer), particular molecular classes, and ion intensity. The results suggest that the choices made at the peak selection step have a significant impact in the visual interpretation of the results of either dimensionality reduction or clustering techniques and consequently in any downstream analysis that relies on these. Of particular significance, the results of this work show that while using the most abundant ions can result in interesting structure-related segmentation patterns that correlate well with histological features, using a smaller number of ions specifically selected based on prior knowledge about the biochemistry of the tissues under investigation can result in an easier-to-interpret, potentially more valuable, hypothesis-confirming result. Findings presented will help researchers understand and better utilize unsupervised machine learning approaches to mine high-dimensionality data.

Chemicals in commerce or under development must be assessed for genotoxicity; assessment is generally conducted using validated assays (e.g. Tk mouse lymphoma assay) as part of a regulatory process. Currently, the MutaMouse FE1 cell mutagenicity assay is undergoing validation for eventual use as a standard in vitro mammalian mutagenicity assay. FE1 cells have been shown to be metabolically competent with respect to some cytochrome P450 (CYP) isozymes; for instance, they can convert the human carcinogen benzo[a]pyrene into its proximate mutagenic metabolite. However, some contradictory results have been noted for other genotoxic carcinogens that require two-step metabolic activation (e.g. 2-acetylaminofluorene and 2-amino-3-methylimidazo[4,5-f]quinoxaline). Here, we examined three known or suspected human carcinogens, namely acrylamide, 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) and 4-aminobiphenyl (4-ABP), together with their proximate metabolites (i.e. glycidamide, N-OH-PhIP and N-OH-4-ABP), to aid in the validation of the FE1 cell mutagenicity assay. Assessments of the parent compounds were conducted both in the presence and absence of an exogenous metabolic activation mixture S9; assessments of the metabolites were in the absence of S9. The most potent compound was N-OH-PhIP -S9, which elicited a mutant frequency (MF) level 5.3-fold over background at 5 µM. There was a 4.3-fold increase for PhIP +S9 at 5 µM, a 1.7-fold increase for glycidamide −S9 at 3.5 mM and a 1.5-fold increase for acrylamide +S9 at 4 mM. Acrylamide −S9 elicited a marginal 1.4-fold MF increase at 8 mM. Treatment with PhIP −S9, 4-ABP ±S9 and N-OH-4-ABP −S9 failed to elicit significant increases in lacZ MF with any of the treatment conditions tested. Gene expression of key CYP isozymes was quantified by RT-qPCR. Cyp1a1, 1a2 and 1b1 are required to metabolise PhIP and 4-ABP. Results showed that treatment with both compounds induced expression of Cyp1a1 and Cyp1b1 but not Cyp1a2. Cyp2e1, which catalyses the bioactivation of acrylamide to glycidamide, was not induced after acrylamide treatment. Overall, our results confirm that the FE1 cell mutagenicity assay has the potential for use alongside other, more traditional in vitro mutagenicity assays.